A Forward-Genetic Screen and Dynamic Analysis of Lambda Phage Host-Dependencies Reveals an Extensive Interaction Network and a New Anti-Viral Strategy

Latently infecting viruses are an important class of virus that plays a key role in viral evolution and human health. Here we report a genome-scale forward-genetics screen for host-dependencies of the latently-infecting bacteriophage lambda. This screen identified 57 Escherichia coli (E. coli) genes—over half of which have not been previously associated with infection—that when knocked out inhibited lambda phage's ability to replicate. Our results demonstrate a highly integrated network between lambda and its host, in striking contrast to the results from a similar screen using the lytic-only infecting T7 virus. We then measured the growth of E. coli under normal and infected conditions, using wild-type and knockout strains deficient in one of the identified host genes, and found that genes from the same pathway often exhibited similar growth dynamics. This observation, combined with further computational and experimental analysis, led us to identify a previously unannotated gene, yneJ, as a novel regulator of lamB gene expression. A surprising result of this work was the identification of two highly conserved pathways involved in tRNA thiolation—one pathway is required for efficient lambda replication, while the other has anti-viral properties inhibiting lambda replication. Based on our data, it appears that 2-thiouridine modification of tRNAGlu, tRNAGln, and tRNALys is particularly important for the efficient production of infectious lambda phage particles

Determining Host Metabolic Limitations on Viral Replication via Integrated Modeling and Experimental Perturbation

<div><p>Viral replication relies on host metabolic machinery and precursors to produce large numbers of progeny - often very rapidly. A fundamental example is the infection of <em>Escherichia coli</em> by bacteriophage T7. The resource draw imposed by viral replication represents a significant and complex perturbation to the extensive and interconnected network of host metabolic pathways. To better understand this system, we have integrated a set of structured ordinary differential equations quantifying T7 replication and an <em>E. coli</em> flux balance analysis metabolic model. Further, we present here an integrated simulation algorithm enforcing mutual constraint by the models across the entire duration of phage replication. This method enables quantitative dynamic prediction of virion production given only specification of host nutritional environment, and predictions compare favorably to experimental measurements of phage replication in multiple environments. The level of detail of our computational predictions facilitates exploration of the dynamic changes in host metabolic fluxes that result from viral resource consumption, as well as analysis of the limiting processes dictating maximum viral progeny production. For example, although it is commonly assumed that viral infection dynamics are predominantly limited by the amount of protein synthesis machinery in the host, our results suggest that in many cases metabolic limitation is at least as strict. Taken together, these results emphasize the importance of considering viral infections in the context of host metabolism.</p> </div

Infected host fluxes on tryptone media.

<p>(A) Flux dynamics are displayed for a subset of the metabolic network map. Arrows representing reactions and the subplots of flux through those reactions are colored according to clustering of flux dynamics. Positive flux values correspond to the reaction direction indicated by the colored arrowhead, negative flux direction is depicted with light grey barbs. Asterisks (*) represent an abbreviation of the arrow for uptake from media. Metabolite abbreviations are consistent with FBA model definition. For clustering, fluxes were treated as vectors with (1-correlation) as distance, and clustered using average hierarchical grouping with a cutoff height of 0.25. Clusters with fewer than ten members appear in black, and clusters with constant dynamics are highlighted in grey. All nonzero fluxes in any media (tryptone, glucose, succinate, and acetate) were included in the flux clustering so that cluster designation and color coding is consistent across media and figures. Maps for media other than tryptone are included in the SI. (B) Select flux dynamics expanded for clarity ordered to exemplify host flux changes driven by viral dynamics: (i) host amino acid synthesis, (ii) major viral capsid protein synthesis, (iii) host nucleotide phosphorylation, (iv) viral digestion of host genome to dNMPs, (v) purine biosynthesis, (vi) viral mRNA synthesis, (vii) viral genome synthesis, (viii) host cell envelope biosynthesis, (ix) host biomass accumulation.</p

Variation in the limiting factor for phage production across host growth rates.

<p>Modeling results overlaid with experimental phage production measurements. The machinery-feasible region represents phage production values from T7 ODEs alone, with the growth rate supplied to correlations for availability of the host replication machinery; phage production values above the machinery-feasible boundary are considered machinery infeasible. The upper boundary of the metabolically feasible region was calculated using the integrated simulation, but with access to excess host replication factors, which we simulated by multiplying the host growth rate from FBA by a factor of 1.25 when it was passed to the T7 ODE host machinery correlations. Growth rate variation for calculating limitation boundaries and integrated simulation was evaluated with a set of modified flux bounds, with most growth rate sampling values simulated with both carbon and oxygen limitation, which produced essentially identical phage production predictions (resulting points lie within width of the line displayed). Error bars are standard deviation of n = 3.</p

Model approaches, scopes, and additions used in the current integration.

<p>(A) The computational methods and the organisms represented by previous modeling efforts that are combined in this study. (B) The additional reactions constructed in this study for the purpose of translating T7 ODE reaction rates into host metabolite use. Shown at the top for each category is a schematic of metabolite connections to host metabolism, and under it the full stoichiometric reaction, which may be a formula based on nucleotide or amino acid sequence (the gene designations taking both decimal and integer values in correspondence with the naming of T7 genes <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002746#pcbi.1002746-Studier2" target="_blank">[36]</a>, a total of n = 59 included). Assumptions made in formulating the reactions are expanded in Methods and SI, and the metabolite abbreviations used are consistent with the FBA model definition.</p

Host population and phage population time courses.

<p>(A) Dynamic time courses of experimental host population data uninfected (line is mean of n = 2) and infected cultures (line is mean of n = 3); an immediate drop in population density occurs when the solution of phage is added at , due to dilution. Initial infection multiplicity was 0.1. (B) Measured and simulated phage production per infected host in tryptone broth media (circles are mean, error bars shown are the standard deviation, n = 3). Simulation presented for the integrated model and T7 ODEs alone simulated at . (C) Expanded comparison of the simulated concentrations of critical phage replication machinery and phage virion components compared to T7 ODEs alone. Gene Product 1 is the T7 RNA polymerase; Gene product 10A is the major capsid protein.</p

Comparing normalized infected host flux dynamics spark-lines for all four media.

<p>(A) Metabolic map and normalized flux dynamics for tryptone, glucose M9, succinate M9, and acetate M9 media. Flux values were shifted to the uninfected value (), and then normalized to their maximum magnitude on each medium; zero (initial) value is indicated by a grey horizontal line. Metabolite abbreviations are consistent with FBA model definition. (B) Expansion of a selected subset of normalized fluxes. Host cell envelope synthesis (i), and biomass accumulation (ii) decrease similarity across media. Purine synthesis (iii) exhibits dynamic similarity across media. Glycolysis (iv) is observed on glucose while gluconeogenisis occurs on other media. Amino acid synthesis (v) increases on minimal media but not on amino acid-rich tryptone; and the citric acid cycle (vi) demonstrates similarity in dynamic flux change timing, but differences in scaling and direction.</p

Format and method for the integrated simulation.

<p>(A) The combined host-viral form of the integrated FBA problem is a stoichiometric matrix (Stoich.) that can be considered as blocks: left, the independent host stoichiometric matrix; right, viral reactions consuming host metabolites. The combined matrix may be further organized by host metabolites that do not supply viral reactions (rows of the matrix in the upper right) and host metabolites that are consumed by viral reactions (rows at the bottom aligned with Host-Viral Stoich). The vector of fluxes contains host reaction rates at the top and viral reaction fluxes at the bottom to multiply properly with the host-left and viral-right organization of reactions in the stoichiometric matrix. Accumulation is allowed at the intersections of host viral metabolism (Met. Accumulation; right), but the steady-state assumption is enforced for host-only metabolites (0). A simplified flowchart (B) of the algorithm for integrated simulations, where Initialize indicates the definition of media nutritional conditions and the start of iterations across time, simulating at each integration time point the individual T7 ODEs and <i>E. coli</i> FBA, then reconciling the viral rate metabolite demand with host network state supply (Allocate). Both models are then recalculated to incorporate information on their mutual constraint (Revised Viral Demand, and Infected Host Fluxes). Update of environmental information and regulatory constraints at the initiation of each integration step (not specifically denoted on figure) further constrains the host-viral system.</p

Measured and simulated phage production.

<p>Shown per infected host, across time, experiment compared to model predictions for integrated model system, and the T7 ODEs alone, on M9 minimal media with glucose, succinate, or acetate as carbon source (growth rates for T7 ODEs alone are , respectively). Error bars are standard deviation of n = 3. For glucose and succinate media the T7 ODEs time course is not visible because it falls directly beneath the integrated simulation line. The lower right panel quantifies the goodness of fit of the integrated simulation and the T7 ODEs alone to experimental observations using normalized mean squared error.</p